Ilizarov technique in the treatment of bone defects of the radius and ulna: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose The objective of this systematic review and meta-analysis was to assess the efficacy of the Ilizarov method in the treatment of radius and ulna bone defects. Methods The PubMed, Embase, Web of Science, Cochrane Library, Ovid MEDLINE, and Scopus databases were searched for articles published up to May 2023. The quality of the studies was evaluated using a modified version of the Newcastle–Ottawa scale. The effect size and confidence intervals at 95% for the main results were calculated. The heterogeneity was evaluated. The demographic data, defect size (DS), external fixation time (EFT), external fixation index (EFI), and complications were extracted and analyzed using the Stata version 16. Results This meta-analysis identified and included seven studies involving 98 patients. The union rate of 100% was reported in all studies. According to the findings of the single-arm meta-analysis, the pooled DS was 3.42 cm (95% CI [2.64, 4.21], I2 = 53.5%, P = 0.045), EFT was 148.43 days (95% CI [97.49, 199.38], I2 = 91.9%, P = 0.000), and EFI was 41.32 days/cm (95% CI [35.72, 46.91], I2 = 62.2%, P = 0.021). Pin tract infection was the most common complication, as reported in six studies. Conclusion The findings of the present meta-analysis indicate that the Ilizarov technique is a successful treatment option for bone defects in the radius and ulna. This method has demonstrated efficacy in achieving expected clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".